Position/Title: PhD Student
Office: ANNU 043
- University of Guelph - Doctor of Philosophy in Animal Biosciences (Present)
- University of Alberta - Masters of Science in Agriculture, Food, and Nutritional Sciences, Animal Science (2020)
- University of Alberta - Bachelor of Science with Honours in Molecular Genetics (2016)
My experience with the livestock industry began at the University of Alberta where I completed a MSc in Animal Science, and ever since, I have taken every opportunity to immerse myself in the industry. My thesis focused on genomic selection in pigs, with the goal to identify the genetic and biological factors underlying meat and carcass quality traits. In addition to my thesis work, I had the opportunity to complete an 18-month internship (MITACS accelerate fellowship) with Hypor Inc., where I explored many areas of pig production, including the farms, abattoirs, and industry offices. After I completed my MSc I had the opportunity to work collaboratively with Hypor Inc. and Animal Inframetrics. Animal Inframetric is an Alberta-based company that works to develop and commercially implement infrared thermography (IRT) technologies as a high-throughput measure of animal phenotypes. Here we compared IRT traits to currently used measures of feed efficiency to assess whether they are suitable indicators for the genetic improvement of feed efficiency in pigs. This project furthered my experience with innovative technologies, 'big data', and genetic analysis. Through these experiences I have recognized that I am interested in using breeding and genetics to find practical and innovative solutions to the present-day challenges experienced by the livestock industry.
My PhD will be a three-year project working under the supervision of Dr. Angela Cánovas and Dr. Flavio Schenkel. The project is an industry collaboration between the University of Guelph and AgSights.
High-throughput technologies will be key for beef cattle producers to improve the genetics of their herds and maintain competitive in the future meat market. However, these technologies increase the size of the datasets and the computational demands required for their analysis. Efficient and flexible genetic evaluations systems (GES) are needed to convert 'big data' into comprehensive results that can be utilized by producers. AgSights provides a GES that is easy to use but it must be updated in order to be able to manage high-throughput data. Therefore, this project aims to update AgSights GES, 1) to be flexible in its ability to efficiently evaluate novel and high-throughput phenotypes, and, 2) to assess the feasibility of including genomic information in a single-step genomic evaluation procedure for muti-breed and crossbred beef cattle. The results of this study will be incorporated into AgSights Go360|bioTrack tool for the selection of high-throughput phenotypes in beef cattle. Overall, this project will increase the economic value of Canadian beef and also facilitate the uptake of technology by the industry in the future.